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Catching Bugs in Software Rajeev Alur Systems Design Research Lab University of Pennsylvania www.cis.upenn.edu/~alur/ Software Reliability Software bugs are pervasive Bugs can be expensive Bugs can cost lives Bulk of development cost is in validation, testing, bug fixes Old problem that just won’t go away Many approaches and decades of research Systematic testing Programming languages technology (e.g. types) Formal methods (specification and verification) Grand challenge for computer science: Tools for designing “correct” software software/model correctness specification Verifier Yes/proof No/bug Correctness is formalized as a mathematical claim to be proved or falsified rigorously always with respect to the given specification A brief history of formal verification 1. Structured programs; Hoare logic; 1969 2. Network protocols; State-space search; 1990 3. Cache coherency protocols; Symbolic search; 1995 4. Device drivers; Automated abstraction; 2001 1. Program Verification Hoare logic for formalizing correctness of structured programs (late 1960s) Typical examples: sorting, graph algorithms Specification for sorting Permute(A,B): array B is a permutation of elements in array A Sorted(A): for 0<i<n, A[i]<=A[i+1] Function sort is correct if following holds {True} B := sort(A) {Permute(A,B)&Sorted(B)} Provides calculus for pre/post conditions of structured programs Sample Proof: Bubble Sort Key to proof: BubbleSort (A : array[1..n] of int) { B = A : array[1..n] of int; Finding suitable for (i=0; i<n; i++) { loop invariants Permute(A,B) Sorted(B[n-i,n]) for 0<k<=n-i-1 and n-i<=k’<=n B[k]<=B[k’] for (j=0; j<n-i; j++) { Permute(A,B), Sorted(B[n-i,n], for 0<k<=n-i-1 and n-i<=k’<=n B[k]<=B[k’] for 0<k<j B[k] <= B[j] if (B[j]>B[j+1]) swap(B,j,j+1) } }; return B; } Program Verification Powerful mathematical logic (e.g. first-order logic, Higher-order logics) needed for formalization Automation extremely difficult Finding proof decomposition requires great expertise Alive and well, but not booming Contemporary theorem provers: HOL, PVS, ACL2 provide decision procedures and tactics for decomposition Main applications: Microprocessor verification, Correctness of JVM… 2. Protocol Analysis Automated analysis of finite-state protocols Network protocols, Distributed algorithms Great progress in the last 20 years Protocol modeled as communicating finite-state processes Correctness specified using temporal logic Verification performed automatically to reveal errors Highly optimized state-space search techniques Model checker SPIN from Bell Labs ACM Software Systems award (2001) Success in finding high-quality bugs in real systems (NASA space shuttle, Lucent’s Pathstar switch) Example: X.21 Communication Protocol State-space Explosion !! Analysis is basically a reachability problem in a graph Nodes are states, where each state gives values of all the variables of all the communicating processes An edge represents execution of a single action of one of the processes (asynchronous communication) Size of graph grows exponentially as the number of bits required for state encoding, but… Graph is constructed only incrementally, on-the-fly Clever hashing and state compaction techniques Many techniques for exploiting structure: symmetry, data independence, partial order reduction … Millions of states can be explored quickly to reveal bugs Great flexibility in modeling Abstract many details, simplify Scale down parameters (buffer size, number of network nodes…) 3. Symbolic Model Checking Constraint-based analysis of Boolean systems Cache coherency protocols, Memory controllers,… Active in the past 12 years Symbolic Boolean representations (propositional formulas, BDDs) used to encode system dynamics Correctness specified using temporal logic CTL Fix-point computation over state sets Highly optimized memory management Model checker SMV from CMU ACM Kannellakis Theory in Practice Award (1999) Success in finding high-quality bugs in hardware applications (VHDL/Verilog code) Cache consistency: Gigamax Real design of a distributed multiprocessor Global bus UIC UIC M UIC P M P Cluster bus Read-shared/read-owned/write-invalid/write-shared/… Deadlock found using SMV Similar successes: IEEE Futurebus+ standard, IBM/Intel/Motorola… Symbolic Reachability Problem Model variables X ={x1, … xn} Each var is of finite type, say, boolean Initialization: I(X) condition over X Update: T(X,X’) How new vars X’ are related to old vars X as a result of executing one step of the program Target set: F(X) Computational problem: Can F be satisfied starting with I by repeatedly applying T ? Graph Search problem Symbolic Solution Data type: region to represent state-sets R:=I(X) Repeat If R intersects T report “yes” else if R contains Post(R) report “no” else R := R union Post(R) Post(R(X))= (Exists X. R(X) and T(X,X’))[X’ -> X] Operations needed: union, intersection, test for inclusion/emptiness, projection, renaming Binary Decision Diagrams Popular representations for Boolean functions 0 0 0 c a 0 1 0 d 1 b 1 1 1 Like a decision graph No redundant nodes No isomorphic subgraphs Variables tested in fixed order Function: (a and b) or (c and d) Key properties: Canonical! Size depends on choice of ordering of variables Operations such as union/intersection are efficient Symbolic Search Techniques Size of BDDs can explode during search, and is quite unpredictable Years of research leading to plethora of heuristics Significant industrial interest In-house groups: Cadence, Synopsis, IBM, NEC… Commercial model checkers/verification consultants Recent focus: SAT solvers Checking whether F can be reached within k steps can be formulated as a satisfiability of a propositional formula with nk variables Extremely fast solvers such as zChaff (from Princeton) can solve problems with 1000 vars fast ! SAT + BDD can be combined to great effects 4. Software Model Checking via Abstraction Can we apply model checking to C programs? SPIN approach is fine for analyzing models, but constructing models is expensive, and models have no relation to code Given a program P, build an abstract finite-state (Boolean) model A such that set of behaviors of P is a subset of those of A (conservative abstraction) Basic ideas around for a while, but all components put together effectively only recently by Microsoft Research team in the project SLAM Shown to be effective on Windows device drivers, Linux source code (about 10K lines of code) Program Abstraction int x, y; if x>0 { ………… y:=x+1 ……….} else { ………… y:=x+1 ……….} Predicate Abstraction bx: x>0; by : y>0 bool bx, by; if bx { ………… by:=true ……….} else { ………… by:={true,false} ……….} Verification Example Does this code obey the locking spec? do { KeAcquireSpinLock(); Rel nPacketsOld = nPackets; Acq Unlocked Locked Rel Acq Error Specification if(request){ request = request->Next; KeReleaseSpinLock(); nPackets++; } } while (nPackets != nPacketsOld); KeReleaseSpinLock(); Initial Abstraction do { KeAcquireSpinLock(); U L if(*){ L L KeReleaseSpinLock(); U L U L U U E } } while (*); KeReleaseSpinLock(); Model checking boolean program Using BDDs Feasibility Analysis do { KeAcquireSpinLock(); U Is error path feasible in C program? Requires theorem prover for constraint propagation L nPacketsOld = nPackets; L L U L U L U U E if(request){ request = request->Next; KeReleaseSpinLock(); nPackets++; } } while (nPackets != nPacketsOld); KeReleaseSpinLock(); Predicate Discovery b : (nPacketsOld == nPackets) do { KeAcquireSpinLock(); U Add new predicate to boolean program New techniques L nPacketsOld = nPackets; b = true; L L U L U L U U E if(request){ request = request->Next; KeReleaseSpinLock(); nPackets++; b = b ? false : *; } } while (nPackets != nPacketsOld); !b KeReleaseSpinLock(); Revised Abstraction b : (nPacketsOld == nPackets) do { KeAcquireSpinLock(); U L b = true; b L if(*){ b L b U b L b L b U !b U KeReleaseSpinLock(); b = b ? false : *; } } while ( !b ); KeReleaseSpinLock(); Model checking refined boolean program Abstraction Based Techniques Tools for verifying source code combine many techniques Program analysis techniques such as slicing Abstraction Model checking Refinement from counter-examples New challenges for model checking (beyond finite-state reachability analysis) Recursion gives pushdown control Pointers, dynamic creation of objects, inheritence…. A very active and emerging research area Research in Formal Methods software Modeling languages Hierarchy, recursion Real-time, Hybrid Stochastic model correctness specification Bridging the gap Model extraction Model-based design: from models to code Decision procedures Algorithms engineering Automated abstraction Compositional analysis Verifier proof bug Temporal logics Automata From requirements to specs Current Research Projects Foundations Analysis of context-free models Stochastic hybrid systems Decision problems for timed automata Algorithms Engineering Combining SAT, BDDs, Abstraction Symbolic solutions to games Model-based design From hybrid automata to embedded software From state-machine models to Java card policies Software verification for Java classes Classical Model Checking Both model M and specification S are regular (finite-state) M as a generator of all possible behaviors S as an acceptor of “good” behaviors (verification is language inclusion of M in S) or as an acceptor of “bad” behaviors (verification is checking emptiness of intersection of M and S) Typical specifications (using automata or temporal logic) Safety: Always not ( both P1 and P2 have write-exclusive copy) Liveness: Always (if P1 requests, eventually it gets response) Robustness of theory of regular languages helps in many ways M can be product of several components (closure under intersection) For liveness properties, one needs to consider automata over infinite words, but corresponding theory of omega-regular languages is well developed and well understood Boolean Programs main() { bool y; … x = P(y); … z = P(x); … } bool P(u: bool) { … return Q(u); } bool Q(w: bool) { if … else return P(~w) } Recursive State Machines A1 A2 A2 A2 A3 A3 Entry-point A3 Box (superstate) A1 Exit-point Model Checking of Recursive Models Control-flow requires stack, so model M defines a context-free language Algorithms exist for checking regular specifications against context-free models Emptiness of pushdown automata is solvable Product of a regular language and a context-free language is context-free But, checking context-free spec against a context-free model is undecidable! Context-free languages are not closed under intersection Inclusion as well as emptiness of intersection undecidable Are Context-free Specs Interesting? Classical Hoare-style pre/post conditions If p holds when procedure A is invoked, q holds upon return Total correctness: every invocation of A terminates Integral part of emerging standard JML Stack inspection properties (security/access control) If a variable x is being accessed, procedure A must be in the call stack Above requires matching of calls with returns, or finding unmatched calls Recall: Language of words over [, ] such that brackets are well matched is not regular, but context-free Caret for Context-free Specifications Caret: Temporal Logic of Calls and Returns [AEM03] Context-free extension of Pnueli’s Linear Temporal Logic LTL Allows specification of pre/post conditions Allows specification of stack inspection properties Main result: Checking Caret specifications against a context-free model is decidable Polynomial in the size of the model and exponential in the size of formula (as in case of classical model checking) Proof technique: Product of pushdown model M and Caret specification S is again a pushdown automaton Key to success: The notion of calls and returns is the same for M as well as S Caret Definition Interpreted over “structured” words in which positions are marked with calls { and returns } p’=Always(p or q) p q’ {q {r p r q’ q {p p p} r q’ p’ p’ p’ q} p p q’=Next(q) Caret provides classical temporal operators such as Next and Always Caret Abstract Operators Abstract versions of operators jump from a call to the matching return p’=abstract-always(p or q) p’ p’ p {q q’ p’ {r q’ p r q {p p’ p q’ p’ p} r q’ Sample specification: pre/post: Always( p & call -> abstract-next q ) p’ p’ p’ q} p p q’=abstract-next(q) Visibly Pushdown Languages [AM03] Subclass of context-free languages that is suitable for program analysis / algorithmic verification Alphabet is structured: Symbols are tagged with calls and returns A visibly pushdown automaton’s moves are constrained by input If current symbol is a call, it must push If current symbol is a return it must pop Else it can only update control state Class of languages defined by these automata is very robust Closed under union, intersection, complement, Kleene-*. Emptiness, inclusion, equivalence decidable Alternative characterizations: Embeddings of regular tree languages, Monadic Second Order theory with a binary matching predicate Caret is a subset of visibly pushdown languages Synthesis of Behavioral Interfaces Behavioral type of a class specifies the allowed sequences of method calls Type for a file class may be (open; (read+open)*;close)* Can we synthesize this type automatically? Given source code for the class implementation Construct a regular language over the method calls so that a particular exception is never raised This is useful for compositional verification also: behavioral interface is a suitable abstraction of the class Proposed route (ongoing project) Use abstraction to get a finite-state model Solve a symbolic game to get the most general strategy for invoking methods to keep the abstract model “safe” Extract interface type from the game solution AbstractList.ListItr public Object next() { … lastRet = cursor++; …} public Object prev() { … lastRet = cursor; …} public void remove() { if (lastRet==-1) throw new IllegalExc(); … lastRet = -1; …} public void add(Object o) { … lastRet = -1; …} Behavioral Interface Start next add next,prev Safe Unsafe remove,add add next,prev Game in Abstracted Program next prev From black states, Player0 gets to choose the input method call From purple states, Player1 gets to choose a path in the abstract program till call returns Objective for Player0: Ensure error states (from which exception can be rasied) are avoided Winning strategy: Correct method sequence calls Challenges Techniques for generating finite-state abstractions How to solve large games symbolically? In fact, a partial information game (Player0 should choose the next method call only based on values returned so far) How to construct an understandble behavioral type from the winning strategy? Abstraction refinement If Player0 does not invoke any method, exceptions can never be raised How to refine the current abstraction based on quality of current behavioral type? Integrating all these into a working tool